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Found 9 papers, 1 papers with code
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
Recent hybrid architectures have combined eidetic and fading memory, but with limitations that do not allow the designer or the learning process to seamlessly modulate the two, nor to extend the eidetic memory span.
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models
We present Diffusion Soup, a compartmentalization method for Text-to-Image Generation that averages the weights of diffusion models trained on sharded data.
Re-visiting Skip-Gram Negative Sampling: Dimension Regularization for More Efficient Dissimilarity Preservation in Graph Embeddings
In this work, we show that node-wise repulsion is, in aggregate, an approximate re-centering of the node embedding dimensions.
Learning Rich Rankings
Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e. g. the Mallows and Plackett-Luce models, that define distributions centered around a single total ordering.
HandsOff: Labeled Dataset Generation With No Additional Human Annotations
Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets.
RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems
We evaluate RecXplainer on five real-world and large-scale recommendation datasets using five different kinds of recommender systems to demonstrate the efficacy of RecXplainer in capturing users' preferences over item attributes and using them to explain recommendations.
Contrastive Learning for Interactive Recommendation in Fashion
Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms.
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice
The Multinomial Logit (MNL) model and the axiom it satisfies, the Independence of Irrelevant Alternatives (IIA), are together the most widely used tools of discrete choice.
Discovering Context Effects from Raw Choice Data
An important class of such contexts is the composition of the choice set.
